Modern applications including social media applications, mobile applications, and analytics platforms, for example, require significant storage resources to host an increasing amount of data. Data center and storage administrators generally prefer to allocate the most suitable storage resources that meet the needs and service level objectives (SLOs) of these applications. As SLOs change and/or data ages over time, administrators may desire to move data from one platform to another in view of the characteristics or capabilities of the platforms, for example.
Data migration between heterogenous storage platforms is currently handled by host system software in the source platform, which initiates and coordinates the copy operation from a source to a destination. Accordingly, migration solutions for heterogenous storage platforms incur non-trivial central processing unit (CPU) overhead since host system CPUs must read data from the source storage servers and send the read data to the destination storage platform.
Additionally, storage networks often become overloaded during data migration between heterogeneous storage platforms since each transfer requires data movement from the source storage server of the source storage platform to the host system and then to the destination storage platform. While some optimization have been developed, these optimizations require customized hardware and/or application programming interfaces (APIs) or are only operable to migrate data using certain network protocols.